Multi-Label Dimensionality Reduction
Inbunden, Engelska, 2013
AvLiang Sun,Shuiwang Ji,Jieping Ye,USA) Sun, Liang (Arizona State University, Tempe,USA) Ji, Shuiwang (Arizona State University, Tempe,USA) Ye, Jieping (Arizona State University, Tempe
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Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks a unified treatment of multi-label dimensionality reduction that incorporates both algorithmic developments and applications. Addressing this shortfall, Multi-Label Dimensionality Reduction covers the methodological developments, theoretical properties, computational aspects, and applications of many multi-label dimensionality reduction algorithms. It explores numerous research questions, including: How to fully exploit label correlations for effective dimensionality reductionHow to scale dimensionality reduction algorithms to large-scale problemsHow to effectively combine dimensionality reduction with classificationHow to derive sparse dimensionality reduction algorithms to enhance model interpretabilityHow to perform multi-label dimensionality reduction effectively in practical applicationsThe authors emphasize their extensive work on dimensionality reduction for multi-label learning. Using a case study of Drosophila gene expression pattern image annotation, they demonstrate how to apply multi-label dimensionality reduction algorithms to solve real-world problems. A supplementary website provides a MATLAB® package for implementing popular dimensionality reduction algorithms.
Produktinformation
- Utgivningsdatum2013-11-04
- Mått156 x 234 x 17 mm
- Vikt540 g
- FormatInbunden
- SpråkEngelska
- SerieChapman & Hall/CRC Machine Learning & Pattern Recognition
- Antal sidor208
- FörlagTaylor & Francis Inc
- ISBN9781439806159